Animal weight is a significant indicator of animal health, development, and likely yield. It is also useful to know the weight of animal before administering medicine, as dosage amounts are typically determined by the animal's estimated weight.
Cattle and other livestock are conventionally weighed by being placed on a scale. Typically, the animal is forced through a narrow passageway called a “cattle chute” onto a scale. Then, the animal is clamped from both sides with a squeeze scale. The process agitates the animal. Transportation of the animal to the scale also stresses the animal. During the time the animal is transported to and squeezed into the scale, the animal often loses weight. Sometimes, aggregate pen scales—some costing about $75,000—are used.
There is a need for a contactless mass or weight estimation system that avoids aggravating the animals. There is also a need for a relatively low-cost system that does not require an elaborate non-mobile wrap-around-the-animal setup of cameras and sensors and does not require wrap-around three-dimensional modeling.
U.S. Pat. No. 4,963,035 to McCarthy et al. discloses an image-processing-based fish sorting machine. The inventor suggests, on column 6, lines 25-29, that the machine could, as one of many possible functions, estimate the weight of a fish as a function of the area of the fish on an image. McCarthy et al. does not teach or suggest fitting a multi-dimensional virtual fish model having configurable shape parameters to the fish image, or of estimating the weight of the fish as a function of any adjusted-to-best-fit shape parameters of a virtual model.
U.S. Pat. No. 5,576,949 to Scofield et al. discloses a system to evaluate the “economic potential” of an animal, based on several sensed characteristics, including images of the animal and a weight scale. Although the system includes a conventional weight scale, Scofield et al. briefly remarks, at col. 33, lines 52-55, that the weight could alternatively be estimated from the height and width measurements obtained from captured images of the animal. Scofield et al. does not, however, teach or suggest fitting a multi-dimensional virtual animal model having configurable shape parameters to the animal image, or of estimating the weight of a live animal as a function of any adjusted-to-best-fit shape parameters of a virtual model.
U.S. Pat. No. 6,549,289 to Ellis teaches projecting a light pattern, such a light grid or pattern of light dots, onto a target animal, photographing the reflected pattern with two cameras, and using triangulation techniques to generate a three-dimensional surface representation of the target animal. Ellis suggests calculating the volume of portions of the target animal from the three-dimensional representation. Ellis does not, however, teach or suggest fitting a multi-dimensional virtual animal model having configurable shape parameters to the image-derived three-dimensional representation of the animal, or of estimating the weight of the target animal as a function of the adjusted-to-best-fit shape parameters of the virtual model.
U.S. Pat. No. 7,128,024 to Doyle, II criticizes animal weight as a poor indicator of animal growth in a cow. Doyle II discloses a system that uses image, ultrasound, and/or acoustic sensors to obtain approximate measurements of the skeletal size of a cow, which the author suggests will better correlate to the ultimate carcass weight of the cow.
U.S. Pat. No. 7,399,320 to Kriesel et al. describes various methods for volumetric and dimensional measurements of livestock. Kriesel discloses an elaborate setup of range cameras and sensors to scan and sense an animal and develop a true three-dimensional (“3D”) representation of the animal. Then, from the three-dimensional data set, Kriesel's system computes the volume of the animal. In Kriesel's system, it is necessary to position the target animal or carcass in a proper position with respect to the cameras.
Also, Kriesel prefers to use a livestock scale 45 to weigh the cow. In column 80, Kriesel remarks that an inferred weight can alternatively be calculated from the true 3D representation of the animal, without the use of scales. But Kriesel adds that an inferred weight “is presently not in use and has not been taught by current patent art.” Moreover, Kriesel does not suggest inferring the cow's total weight from a virtual spatial model of the cow that has been reshaped to fit 3D representation of the animal.
In column 35, Kriesel suggests using a cow model to estimate some of the hidden dimensions of a target cow, some of whose dimensions have been directly determined through image analysis of the cow's non-hidden dimensions. In column 65, Kriesel also suggests scaling an MRI model of a cow or hog to match the target animal in order to estimate the position and size of the targeted animals' internal organs, muscles, and bones, and thereby estimate production yields. But Kriesel does not disclose or suggest that one could, with a reasonable degree of accuracy, estimate the entire weight of a live target animal as a function of the adjusted-to-best-fit shape parameters of a virtual model.